Vaccine acceptance: How to build and maintain trust in immunization
Bibliographic record
Abstract
In Canada, over 80% of parents choose to vaccinate their children. Although this may appear positive, it is one of the lowest vaccination rates in the western world, and does not meet the 95% coverage rate needed to prevent outbreaks of vaccine-preventable diseases such as measles. A recent national immunization survey showed approximately 50% of parents are concerned about potential side-effects from vaccines, 25% believe that a vaccine can cause the disease it was meant to prevent, and 13% think alternative practices could eliminate the need for vaccines. In addition, vaccine hesitancy-defined by its determinants: confidence, complacency and convenience-is on rise. To address the complacency and trust (confidence) components of vaccine hesitancy, four best practices to optimize trust in vaccines and promote vaccine acceptance are presented. The first best practice is to understand the concerns; this is done at a population level via research and at individual level via motivational interviewing. The second best practice is to address these concerns by effectively presenting science-based information. This is done at a population level by communicating research and at an individual level by applying this research to the specific concerns, values and norms of the individual. Third, present immunization as a social norm, both in educational materials and in conversations. Finally, resilience is fostered by planning ahead (both at a population level and for individual practitioners) to manage events that can undermine trust and drive negative vaccine concerns, such as a new vaccine being added to the routine schedule or the emergence of an unexpected adverse event. Building and maintaining public trust in immunization takes time. Healthcare practitioners must keep in mind that while trust is a key element in vaccine acceptance, it is not the only element; convenience and access can also impact vaccine uptake. Nurturing trust is but one part of increasing vaccine acceptance and this brief will focus on strategies to build and nurture trust.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".